Author + information
- Bram Ruijsink, MD, PhDa,b,∗∗ (, )
- Esther Puyol-Antón, PhDa,∗,
- Ilkay Oksuz, PhDa,
- Matthew Sinclair, PhDa,
- Wenjia Bai, PhDc,d,
- Julia A. Schnabel, PhDa,
- Reza Razavi, MD, PhDa,b and
- Andrew P. King, PhDa
- aSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- bDepartment of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, London, United Kingdom
- cBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- dDepartment of Medicine, Imperial College London, London, United Kingdom
- ↵∗Address for correspondence:
Dr. Bram Ruijsink, The Rayne Institute 4th Floor Lambeth Wing, St Thomas Hospital, Westminster Bridge Road, SE1 7EH London, United Kingdom.
Objectives This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output.
Background Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice.
Methods The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps’ ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank.
Results Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects.
Conclusions The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.
- cardiac aging
- cardiac function
- cardiac magnetic resonance
- CMR feature tracking
- machine learning
- quality control
↵∗ Drs. Ruijsink and Puyol-Antón contributed equally to this work and are joint first authors.
This work was supported by the Wellcome EPSRC Centre for Medical Engineering at Kings College London (WT 203148/Z/16/Z), the EPSRC (EP/P001009/1 and EP/R005516/1) and by the NIHR Cardiovascular MedTech Co-operative. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, EPSRC, or the Department of Health. This research has been conducted using the UK Biobank Resource (application 17806) on a GPU generously donated by NVIDIA Corporation. The UK Biobank data are available for approved projects from https://www.ukbiobank.ac.uk/. Dr. Sinclair is an employee of HeartFlow. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received January 30, 2019.
- Revision received April 26, 2019.
- Accepted May 16, 2019.
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